Proceedings of the 35th International Conference on Machine Learning, pages: 2405-2414, PMLR, July 2018 (conference)

Abstract

We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We provide a theoretical explanation regarding why the approach works, showing (for the population setting) that, under a certain assumption, point estimates obtained with this method converge to the true parameter, as recursion proceeds. We have conducted a variety of numerical experiments, including parameter estimation for a real-world pedestrian flow simulator, and show that in most cases our method outperforms existing approaches.

In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018 (inproceedings)Accepted

Abstract

The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn't. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of stochastic gradients, whereas the update magnitude is determined by an estimate of their relative variance. We disentangle these two aspects and analyze them in isolation, gaining insight into the mechanisms underlying ADAM. This analysis also extends recent results on adverse effects of ADAM on generalization, isolating the sign aspect as the problematic one. Transferring the variance adaptation to SGD gives rise to a novel method, completing the practitioner's toolbox for problems where ADAM fails.

2015

Machine Learning in Planning and Control of Robot Motion Workshop at the IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS), pages: , , Machine Learning in Planning and Control of Robot Motion Workshop, October 2015 (conference)

Abstract

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Preliminary results of a low-dimensional tuning problem highlight the method’s potential for automatic controller tuning on robotic platforms.

In this work, we examine several wing designs for a motor-driven, flapping-wing micro air vehicle capable of liftoff. The full system consists of two wings independently driven by geared pager motors that include a spring in parallel with the output shaft. The linear transmission allows for resonant operation, while control is achieved by direct drive of the wing angle. Wings used in previous work were chosen to be fully rigid for simplicity of modeling and fabrication. However, biological wings are highly flexible and other micro air vehicles have successfully utilized flexible wing structures for specialized tasks. The goal of our study is to determine if wing flexibility can be generally used to increase wing performance. Two approaches to lift improvement using flexible wings are explored, resonance of the wing cantilever structure and dynamic wing twisting. We design and test several wings that are compared using different figures of merit. A twisted design improved lift per power by 73.6% and maximum lift production by 53.2% compared to the original rigid design. Wing twist is then modeled in order to propose optimal wing twist profiles that can maximize either wing efficiency or lift production.

In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages: 1706-1711, September 2015 (inproceedings)

Abstract

This paper presents a new soft-bodied millimeterscale swimmer actuated by rotating uniform magnetic fields. The proposed swimmer moves through internal undulatory deformations, resulting from a magnetization profile programmed into its body. To understand the motion of the swimmer, a mathematical model is developed to describe the general relationship between the deflection of a flexible strip and its magnetization profile. As a special case, the situation of the swimmer on the water surface is analyzed and predictions made by the model are experimentally verified. Experimental results show the controllability of the proposed swimmer under a computer vision-based closed-loop controller. The swimmers have nominal dimensions of 1.5×4.9×0.06 mm and a top speed of 50 mm/s (10 body lengths per second). Waypoint following and multiagent control are demonstrated for swimmers constrained at the air-water interface and underwater swimming is also shown, suggesting the promising potential of this type of swimmer in biomedical and microfluidic applications.

Vibration-driven locomotion has been widely used for crawling robot studies. Such robots usually have a vibration motor as the actuator and a fibrillar structure for providing directional friction on the substrate. However, there has not been any studies about the effect of fiber structure on robot crawling performance. In this paper, we develop Fiberbot, a custom made mini vibration robot, for studying the effect of fiber angle on robot velocity, steering, and climbing performance. It is known that the friction force with and against fibers depends on the fiber angle. Thus, we first present a new fabrication method for making millimeter scale fibers at a wide range of angles. We then show that using 30° angle fibers that have the highest friction anisotropy (ratio of backward to forward friction force) among the other fibers we fabricated in this study, Fiberbot speed on glass increases to 13.8±0.4 cm/s (compared to ν = 0.6±0.1 cm/s using vertical fibers). We also demonstrate that the locomotion direction of Fiberbot depends on the tilting direction of fibers and we can steer the robot by rotating the fiber pad. Fiberbot could also climb on glass at inclinations of up to 10° when equipped with fibers of high friction anisotropy. We show that adding a rigid tail to the robot it can climb on glass at 25° inclines. Moreover, the robot is able to crawl on rough surfaces such as wood (ν = 10.0±0.2 cm/s using 30° fiber pad). Fiberbot, a low-cost vibration robot equipped with a custom-designed fiber pad with steering and climbing capabilities could be used for studies on collective behavior on a wide range of topographies as well as search and exploratory missions.

In this work, we examine two design modifications to a tethered motor-driven flapping-wing system. Previously, we had demonstrated a simple mechanism utilizing a linear transmission for resonant operation and direct drive of the wing flapping angle for control. The initial two-wing system had a weight of 2.7 grams and a maximum lift-to-weight ratio of 1.4. While capable of vertical takeoff, in open-loop flight it demonstrated instability and pitch oscillations at the wing flapping frequency, leading to flight times of only a few wing strokes. Here the effect of vertical wing offset as well as an alternative multi-wing layout is investigated and experimentally tested with newly constructed prototypes. With only a change in vertical wing offset, stable open-loop flight of the two-wing flapping system is shown to be theoretically possible, but difficult to achieve with our current design and operating parameters. Both of the new two and four-wing systems, however, prove capable of flying to the end of the tether, with the four-wing system prototype eliminating disruptive wing beat oscillations.

In deterministic optimization, line searches are a standard tool ensuring stability and efficiency. Where only stochastic gradients are available, no direct equivalent has so far been formulated, because uncertain gradients do not allow for a strict sequence of decisions collapsing the search space. We construct a probabilistic line search by combining the structure of existing deterministic methods with notions from Bayesian optimization. Our method retains a Gaussian process surrogate of the univariate optimization objective, and uses a probabilistic belief over the Wolfe conditions to monitor the descent. The algorithm has very low computational cost, and no user-controlled parameters. Experiments show that it effectively removes the need to define a learning rate for stochastic gradient descent.
[You can find the matlab research code under `attachments' below. The zip-file contains a minimal working example. The docstring in probLineSearch.m contains additional information. A more polished implementation in C++ will be published here at a later point. For comments and questions about the code please write to mmahsereci@tue.mpg.de.]

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems